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Big Data applications - EFFORTE Business Forum

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Presentation by Erik Willén

Publié dans : Environnement
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Big Data applications - EFFORTE Business Forum

  1. 1. Big Data applications Efforte Business Forum Erik Willén, Isabelle Bergkvist
  2. 2. Objectives Describe potentials in using, refining and linking existing and new Big Data sources in forestry applications, and evaluate the identified sources of data through a SWOT analysis. Based on these data, create decision-support models for efficient, sustainable and value-creating forestry and forest operations. Demonstrate the potential in keeping and refining information throughout subsequent operations in the forestry process. Validation and pilot tests of project results in relevant environment (hosted by partner companies).
  3. 3. Outlook on Big Databases - forest planning Detailed digital elevation model Soil maps VHR imagery Mobile laser scanning Weather data and models Lidar based forest estimates Road databases Forest machine data
  4. 4. Demonstrated decision support models  Trafficability maps  Operational logging trafficability mapping  Logging trail visualizations and port-harvest quality control  Models for operational planning of forest operations  Yield and wood property forecasting  Models to improve efficiency in silviculture operations
  5. 5. Trafficability maps Depth to Water map 5 26.6.2019
  6. 6. 6 26.6.2019 Static Trafficability map
  7. 7. Dynamic Trafficability map - SpatHy 7 26.6.2019
  8. 8. Layout of main extraction routes Slope and terrain Volume Digital terrain model
  9. 9. 10 26.6.2019 Logging trail visualizations and port-harvest quality control 2D LiDAR for continous rut depth measurement
  10. 10. -15 -10 -5 0 5 10 15 20 25 30 -15 -10 -5 0 5 10 15 20 25 30 Manual Laser LiDAR vs manual: per 5m test blocks and pass Left LiDAR vs manual Right LiDAR vs manual Vihti study Kuru study
  11. 11. 12 26.6.2019Ala-Ilomäki et. al Harvester CAN-bus Harvester CAN-bus data for site trafficability mapping GPS/Glonass - positioning Harvester followed by a loaded fwd up to five passes Harvester courtesy of Ponsse Plc First harvester with and without tracks on two parallel test tracks
  12. 12. 13 26.6.2019Ala-Ilomäki et. al Harvester CAN-bus Motion resistance, rut depth of harvester and forwarder on 1st and 2nd pass
  13. 13. Harvester scheduling
  14. 14. Preferred areas Home Performance Assortiment Supply by roads Type of forest cutting Quantity Assortiment Quantity Radie for actions Priority Average stem Forwarder distance Labour time Target volumes
  15. 15. 1011110010101011100001010101 000101010101111111001010 101111001010101110 00010101 Wood property modelling Wood property models using harvester data for each log Validated in x-rays at sawmills Blue: model Yellow: reference Example: Basic density Fiberwall thickness Fiber length ….
  16. 16. Models to improve efficiency in silviculture operations Plant order tool  Based on harvester data  Actual growth conditions  Root rot frequency  Site index
  17. 17. Conclusions  Operational use of trafficability maps and tools to extract main extraction routes  Digital elevation model spatial resolution critical  Important to act pro-actively to secure data collection  Interesting potential to use Can-Bus data to for trafficability mapping – other options also availible (drones, machine-mounted equipment, ..)  Big data applications opens a wide range of possibilites for research and applications – standardisation crucial!  Previous research now possible to implement with Big Data Streams  Forest machine data key for many applications – start collect standardised data!

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